GPND-AI NULISA: A 15-Protein AI classifier for diagnosis and co-pathology profiling across neurodegenerative diseases.
GPND-AI NULISA: A 15-Protein AI classifier for diagnosis and co-pathology profiling across neurodegenerative diseases.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
St. Louis, US · Author affiliation
Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA.Location evidence
US · Author affiliation · country only
Banner Sun Health Research Institute, Sun City, Arizona, USA.Location evidence
Phoenix, US · Author affiliation
Banner Alzheimer's Institute, Phoenix, Arizona, USA.Location evidence
Gothenburg, SE · Author affiliation
Department of Psychiatry and Neurochemistry Institute of Neuroscience & Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Västergötland, Sweden.Location evidence
Mölndal, SE · Author affiliation
Department of Psychiatry and Neurochemistry Institute of Neuroscience & Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Västergötland, Sweden.Location evidence
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Original abstract
INTRODUCTION: Accurate clinical diagnosis of neurodegenerative diseases remains challenging, particularly when individuals have mixed pathologies. We implemented the generalizable protein-based neurodegenerative disease artificial intelligence (GPND-AI) classifier using the NUcleic acid-Linked Immuno-Sandwich Assay (NULISA) central nervous system (CNS) panel to classify Alzheimer's disease, Parkinson's disease, frontotemporal dementia, dementia with Lewy bodies, and healthy controls, while disentangling mixed pathologies. METHODS: Proteomic and clinical information from the Charles F. and Joanne Knight Alzheimer's Disease Research Center (Knight-ADRC) and Movement Disorder Clinic were used to train and test the GPND-AI classifier. External validation was performed in a Banner Sun Health Research Institute cohort and additional Knight-ADRC samples with neuropathologically confirmed diagnoses. RESULTS: GPND-AI identified 15 proteins that achieve an area under the curve (AUC) of 0.955 and 92.3% accuracy across five diagnostic categories. In validation cohort, predicted co-pathologies significantly correlated with clinical characteristics. DISCUSSION: GPND-AI identified a 15-protein panel that accurately classifies individuals across the four major neurodegenerative diseases. Validation against neuropathology-confirmed diagnoses supports the utility of proteomics-based approaches for mapping disease-specific and co-existing neurodegenerative processes.